Determining connectivity within a community

ABSTRACT

Systems and methods to determine trust scores and/or trustworthiness levels and/or connectivity are described herein. Trust and/or Trustworthiness and/or connectivity may be determined within, among or between entities and/or individuals. Social analytics and network calculations described herein may be based on user-assigned links or ratings and/or objective measures, such as data from third-party ratings agencies. The trust score may provide guidance about the trustworthiness, alignment, reputation, status, membership status and/or influence about an individual, entity, or group. The systems and methods described herein may be used to make prospective real-world decisions, such as whether or not to initiate a transaction or relationship with another person, or whether to grant a request for credit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 13/498,429, filed Mar. 27, 2012, which is anational stage filing under 35 U.S.C. §371 of International ApplicationNo. PCT/CA2010/001531, filed Sep. 30, 2010, which claims the benefit ofthe filing date under 35 U.S.C. §119(e) to U.S. provisional applicationNo. 61/247,343, filed Sep. 30, 2009. The aforementioned, earlier-filedapplications are hereby incorporated by reference herein in theirentireties.

BACKGROUND OF THE INVENTION

This invention relates generally to networks of individuals and/orentities and network communities and, more particularly, to systems andmethods for determining trust scores or connectivity within or betweenindividuals and/or entities or networks of individuals and/or entities.

The connectivity, or relationships, of an individual or entity within anetwork community may be used to infer attributes of that individual orentity. For example, an individual or entity's connectivity within anetwork community may be used to determine the identity of theindividual or entity (e.g., used to make decisions about identity claimsand authentication), the trustworthiness or reputation of the individualor entity, or the membership, status, and/or influence of thatindividual or entity in a particular community or subset of a particularcommunity.

An individual or entity's connectivity within a network community,however, is difficult to quantify. For example, network communities mayinclude hundreds, thousands, millions, billions or more members. Eachmember may possess varying degrees of connectivity information aboutitself and possibly about other members of the community. Some of thisinformation may be highly credible or objective, while other informationmay be less credible and subjective. In addition, connectivityinformation from community members may come in various forms and onvarious scales, making it difficult to meaningfully compare one member's“trustworthiness” or “competence” and connectivity information withanother member's “trustworthiness” or “competence” and connectivityinformation. Also, many individuals may belong to multiple communities,further complicating the determination of a quantifiable representationof trust and connectivity within a network community. Even if aquantifiable representation of an individual's connectivity isdetermined, it is often difficult to use this representation in ameaningful way to make real-world decisions about the individual (e.g.,whether or not to trust the individual).

Further, it may be useful for these real-world decisions to be madeprospectively (i.e., in advance of an anticipated event). Suchprospective analysis may be difficult as an individual or entity'sconnectivity within a network community may change rapidly as theconnections between the individual or entity and others in the networkcommunity may change quantitatively or qualitatively. This analysisbecomes increasingly complex as if applied across multiple communities.

SUMMARY OF THE INVENTION

In view of the foregoing, systems and methods are provided fordetermining the connectivity between nodes within a network communityand inferring attributes, such as trustworthiness or competence, fromthe connectivity. Connectivity may be determined, at least in part,using various graph traversal and normalization techniques described inmore detail below.

In an embodiment, a path counting approach may be used where processingcircuitry is configured to count the number of paths between a firstnode n₁ and a second node n₂ within a network community. A connectivityrating R_(n1n2) may then be assigned to the nodes. The assignedconnectivity rating may be proportional to the number of subpaths, orrelationships, connecting the two nodes, among other possible measures.Using the number of subpaths as a measure, a path with one or moreintermediate nodes between the first node n₁ and the second node n₂ maybe scaled by an appropriate number (e.g., the number of intermediatenodes) and this scaled number may be used to calculate the connectivityrating.

In some embodiments, weighted links are used in addition or as analternative to the subpath counting approach. Processing circuitry maybe configured to assign a relative user weight to each path connecting afirst node n₁ and a second node n₂ within a network community. A userconnectivity value may be assigned to each link. For example, a user orentity associated with node n₁ may assign user connectivity values forall outgoing paths from node n₁. In some embodiments, the connectivityvalues assigned by the user or entity may be indicative of that user orentity's trust in the user or entity associated with node n₂. The linkvalues assigned by a particular user or entity may then be compared toeach other to determine a relative user weight for each link.

The relative user weight for each link may be determined by firstcomputing the average of all the user connectivity values assigned bythat user (i.e., the out-link values). If t_(i) is the user connectivityvalue assigned to link i, then the relative user weight, w_(i), assignedto that link may be given in accordance with:

w=1+(t _(i)− t _(i) )²  (1)

To determine the overall weight of a path, in some embodiments, theweights of all the links along the path may be multiplied together. Theoverall path weight may then be given in accordance with:

w _(path)=Π(w _(i))  (2)

The connectivity value for the path may then be defined as the minimumuser connectivity value of all the links in the path multiplied by theoverall path weight in accordance with:

t _(path) +w _(path) ×t _(min)  (3)

To determine path connectivity values, in some embodiments, a parallelcomputational framework or distributed computational framework (or both)may be used. For example, in one embodiment, a number of core processorsimplement an Apache Hadoop or Google MapReduce cluster. This cluster mayperform some or all of the distributed computations in connection withdetermining new path link values and path weights.

The processing circuitry may identify a changed node within a networkcommunity. For example, a new outgoing link may be added, a link may beremoved, or a user connectivity value may have been changed. In responseto identifying a changed node, in some embodiments, the processingcircuitry may re-compute link, path, and weight values associated withsome or all nodes in the implicated network community or communities.

In some embodiments, only values associated with affected nodes in thenetwork community are recomputed after a changed node is identified. Ifthere exists at least one changed node in the network community, thechanged node or nodes may first undergo a prepare process. The prepareprocess may include a “map” phase and “reduce” phase. In the map phaseof the prepare process, the prepare process may be divided into smallersub-processes which are then distributed to a core in the parallelcomputational framework cluster. For example, each node or link change(e.g., tail to out-link change and head to in-link change) may be mappedto a different core for parallel computation. In the reduce phase of theprepare process, each out-link's weight may be determined in accordancewith equation (1). Each of the out-link weights may then be normalizedby the sum of the out-link weights (or any other suitable value). Thenode table may then be updated for each changed node, its in-links, andits out-links.

After the changed nodes have been prepared, the paths originating fromeach changed node may be calculated. Once again, a “map” and “reduce”phase of this process may be defined. During this process, in someembodiments, a depth-first search may be performed of the node digraphor node tree. All affected ancestor nodes may then be identified andtheir paths recalculated.

In some embodiments, to improve performance, paths may be grouped by thelast node in the path. For example, all paths ending with node n₁ may begrouped together, all paths ending with node n₂ may be grouped together,and so on. These path groups may then be stored separately (e.g., indifferent columns of a single database table). In some embodiments, thepath groups may be stored in columns of a key-value store implementingan HBase cluster (or any other compressed, high performance databasesystem, such as BigTable).

In some embodiments, one or more threshold functions may be defined. Thethreshold function or functions may be used to determine the maximumnumber of links in a path that will be analyzed in a connectivitydetermination or connectivity computation. Threshold factors may also bedefined for minimum link weights, path weights, or both. Weights fallingbelow a user-defined or system-defined threshold may be ignored in aconnectivity determination or connectivity computation, while onlyweights of sufficient magnitude may be considered.

In some embodiments, a user connectivity value may represent the degreeof trust between a first node and a second node. In one embodiment, noden₁ may assign a user connectivity value of l₁ to a link between it andnode n₂. Node n₂ may also assign a user connectivity value of l₂ to areverse link between it and node n₁. The values of l₁ and l₂ may be atleast partially subjective indications of the trustworthiness of theindividual or entity associated with the node connected by the link. Forexample, one or more of the individual or entity's reputation, status,and/or influence within the network community (or some other community),the individual or entity's alignment with the trusting party (e.g.,political, social, or religious alignment), past dealings with theindividual or entity, and the individual or entity's character andintegrity (or any other relevant considerations) may be used todetermine a partially subjective user connectivity value indicative oftrust. A user (or other individual authorized by the node) may thenassign this value to an outgoing link connecting the node to theindividual or entity. Objective measures (e.g., data from third-partyratings agencies or credit bureaus) may also be used, in someembodiments, to form composite user connectivity values indicative oftrust. The subjective, objective, or both types of measures may beautomatically harvested or manually inputted for analysis.

In some embodiments, a decision-making algorithm may access theconnectivity values in order to make automatic decisions (e.g.,automatic network-based decisions, such as authentication or identityrequests) on behalf of a user. Connectivity values may additionally oralternatively be outputted to external systems and processes located atthird-parties. The external systems and processes may be configured toautomatically initiate a transaction (or take some particular course ofaction) based, at least in part, on received connectivity values. Forexample, electronic or online advertising may be targeted to subgroupsof members of a network community based, at least in part, on networkconnectivity values.

In some embodiments, a decision-making algorithm may access theconnectivity values to make decisions prospectively (e.g., before ananticipated event like a request for credit). Such decisions may be madeat the request of a user, or as part of an automated process (e.g., acredit bureau's periodic automated analysis of a database of customerinformation). This prospective analysis may allow for the initiation ofa transaction (or taking of some particular action) in a fluid and/ordynamic manner.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other features of the present invention, its nature andvarious advantages will be more apparent upon consideration of thefollowing detailed description, taken in conjunction with theaccompanying drawings, and in which:

FIG. 1 is an illustrative block diagram of a network architecture usedto support connectivity within a network community in accordance withone embodiment of the invention;

FIG. 2 is another illustrative block diagram of a network architectureused to support connectivity within a network community in accordancewith one embodiment of the invention;

FIGS. 3A and 3B show illustrative data tables for supportingconnectivity determinations within a network community in accordancewith one embodiment of the invention;

FIGS. 4A-4E show illustrative processes for supporting connectivitydeterminations within a network community in accordance with oneembodiment of the invention; and

FIG. 5 shows an illustrative process for querying all paths to a targetnode and computing a network connectivity value in accordance with oneembodiment of the invention.

DETAILED DESCRIPTION

Systems and methods for determining the connectivity between nodes in anetwork community are provided. As defined herein, a “node” may includeany user terminal, network device, computer, mobile device, accesspoint, or any other electronic device. In some embodiments, a node mayalso represent an individual human being, entity (e.g., a legal entity,such as a public or private company, corporation, limited liabilitycompany (LLC), partnership, sole proprietorship, or charitableorganization), concept (e.g., a social networking group), animal, orinanimate object (e.g., a car, aircraft, or tool). As also definedherein, a “network community” may include a collection of nodes and mayrepresent any group of devices, individuals, or entities.

For example, all or some subset of the users of a social networkingwebsite or social networking service (or any other type of website orservice, such as an online gaming community) may make up a singlenetwork community. Each user may be represented by a node in the networkcommunity. As another example, all the subscribers to a particularnewsgroup or distribution list may make up a single network community,where each individual subscriber may be represented by a node in thenetwork community. Any particular node may belong in zero, one, or morethan one network community, or a node may be banned from all, or asubset of, the community. To facilitate network community additions,deletions, and link changes, in some embodiments a network community maybe represented by a directed graph, or digraph, weighted digraph, tree,or any other suitable data structure.

FIG. 1 shows illustrative network architecture 100 used to support theconnectivity determinations within a network community. A user mayutilize access application 102 to access application server 106 overcommunications network 104. For example, access application 102 mayinclude a standard web browser, application server 106 may include a webserver, and communication network 106 may include the Internet. Accessapplication 102 may also include proprietary applications specificallydeveloped for one or more platforms or devices. For example, accessapplication 102 may include one or more instances of an Apple iOS,Android, or WebOS application or any suitable application for use inaccessing application 106 over communications network 104. Multipleusers may access application service 106 via one or more instances ofaccess application 102. For example, a plurality of mobile devices mayeach have an instance of access application 102 running locally on thedevices. One or more users may use an instance of access application 102to interact with application server 106.

Communication network 104 may include any wired or wireless network,such as the Internet, WiMax, wide area cellular, or local area wirelessnetwork. Communication network 104 may also include personal areanetworks, such as Bluetooth and infrared networks. Communications oncommunications network 104 may be encrypted or otherwise secured usingany suitable security or encryption protocol.

Application server 106, which may include any network server or virtualserver, such as a file or web server, may access data sources 108locally or over any suitable network connection. Application server 106may also include processing circuitry (e.g., one or moremicroprocessors), memory (e.g., RAM, ROM, and hybrid types of memory),storage devices (e.g., hard drives, optical drives, and tape drives).The processing circuitry included in application server 106 may executea server process for supporting the network connectivity determinationsof the present invention, while access application 102 executes acorresponding client process. The processing circuitry included inapplication server 106 may also perform any of the calculations andcomputations described herein in connection with determining networkconnectivity. In some embodiments, a computer-readable medium withcomputer program logic recorded thereon is included within applicationserver 106. The computer program logic may determine the connectivitybetween two or more nodes in a network community and it may or may notoutput such connectivity to a display screen or data

For example, application server 106 may access data sources 108 over theInternet, a secured private LAN, or any other communications network.Data sources 108 may include one or more third-party data sources, suchas data from third-party social networking services and third-partyratings bureaus. For example, data sources 108 may include user andrelationship data (e.g., “friend” or “follower” data) from one or moreof Facebook, MySpace, openSocial, Friendster, Bebo, hi5, Orkut,PerfSpot, Yahoo! 360, LinkedIn, Twitter, Google Buzz, Really SimpleSyndication readers or any other social networking website orinformation service. Data sources 108 may also include data stores anddatabases local to application server 106 containing relationshipinformation about users accessing application server 106 via accessapplication 102 (e.g., databases of addresses, legal records,transportation passenger lists, gambling patterns, political and/orcharity donations, political affiliations, vehicle license plate oridentification numbers, universal product codes, news articles, businesslistings, and hospital or university affiliations).

Application server 106 may be in communication with one or more of datastore 110, key-value store 112, and parallel computational framework114. Data store 110, which may include any relational databasemanagement system (RDBMS), file server, or storage system, may storeinformation relating to one or more network communities. For example,one or more of data tables 300 (FIG. 3A) may be stored on data store110. Data store 110 may store identity information about users andentities in the network community, an identification of the nodes in thenetwork community, user link and path weights, user configurationsettings, system configuration settings, and/or any other suitableinformation. There may be one instance of data store 110 per networkcommunity, or data store 110 may store information relating to a pluralnumber of network communities. For example, data store 110 may includeone database per network community, or one database may storeinformation about all available network communities (e.g., informationabout one network community per database table). Parallel computationalframework 114, which may include any parallel or distributedcomputational framework or cluster, may be configured to dividecomputational jobs into smaller jobs to be performed simultaneously, ina distributed fashion, or both. For example, parallel computationalframework 114 may support data-intensive distributed applications byimplementing a map/reduce computational paradigm where the applicationsmay be divided into a plurality of small fragments of work, each ofwhich may be executed or re-executed on any core processor in a clusterof cores. A suitable example of parallel computational framework 114includes an Apache Hadoop cluster.

Parallel computational framework 114 may interface with key-value store112, which also may take the form of a cluster of cores. Key-value store112 may hold sets of key-value pairs for use with the map/reducecomputational paradigm implemented by parallel computational framework114. For example, parallel computational framework 114 may express alarge distributed computation as a sequence of distributed operations ondata sets of key-value pairs. User-defined map/reduce jobs may beexecuted across a plurality of nodes in the cluster. The processing andcomputations described herein may be performed, at least in part, by anytype of processor or combination of processors. For example, varioustypes of quantum processors (e.g., solid-state quantum processors andlight-based quantum processors), artificial neural networks, and thelike may be used to perform massively parallel computing and processing.

In some embodiments, parallel computational framework 114 may supporttwo distinct phases, a “map” phase and a “reduce” phase. The input tothe computation may include a data set of key-value pairs stored atkey-value store 112. In the map phase, parallel computational framework114 may split, or divide, the input data set into a large number offragments and assign each fragment to a map task. Parallel computationalframework 114 may also distribute the map tasks across the cluster ofnodes on which it operates. Each map task may consume key-value pairsfrom its assigned fragment and produce a set of intermediate key-valuepairs. For each input key-value pair, the map task may invoke a userdefined map function that transmutes the input into a differentkey-value pair. Following the map phase, parallel computationalframework 114 may sort the intermediate data set by key and produce acollection of tuples so that all the values associated with a particularkey appear together. Parallel computational framework 114 may alsopartition the collection of tuples into a number of fragments equal tothe number of reduce tasks.

In the reduce phase, each reduce task may consume the fragment of tuplesassigned to it. For each such tuple, the reduce task may invoke auser-defined reduce function that transmutes the tuple into an outputkey-value pair. Parallel computational framework 114 may then distributethe many reduce tasks across the cluster of nodes and provide theappropriate fragment of intermediate data to each reduce task.

Tasks in each phase may be executed in a fault-tolerant manner, so thatif one or more nodes fail during a computation the tasks assigned tosuch failed nodes may be redistributed across the remaining nodes. Thisbehavior may allow for load balancing and for failed tasks to bere-executed with low runtime overhead.

Key-value store 112 may implement any distributed file system capable ofstoring large files reliably. For example key-value store 112 mayimplement Hadoop's own distributed file system (DFS) or a more scalablecolumn-oriented distributed database, such as HBase. Such file systemsor databases may include BigTable-like capabilities, such as support foran arbitrary number of table columns.

Although FIG. 1, in order to not over-complicate the drawing, only showsa single instance of access application 102, communications network 104,application server 106, data source 108, data store 110, key-value store112, and parallel computational framework 114, in practice networkarchitecture 100 may include multiple instances of one or more of theforegoing components. In addition, key-value store 112 and parallelcomputational framework 114 may also be removed, in some embodiments. Asshown in network architecture 200 of FIG. 2, the parallel or distributedcomputations carried out by key-value store 112 and/or parallelcomputational framework 114 may be additionally or alternativelyperformed by a cluster of mobile devices 202 instead of stationarycores. In some embodiments, cluster of mobile devices 202, key-valuestore 112, and parallel computational framework 114 are all present inthe network architecture. Certain application processes and computationsmay be performed by cluster of mobile devices 202 and certain otherapplication processes and computations may be performed by key-valuestore 112 and parallel computational framework 114. In addition, in someembodiments, communication network 104 itself may perform some or all ofthe application processes and computations. For example,specially-configured routers or satellites may include processingcircuitry adapted to carry out some or all of the application processesand computations described herein.

Cluster of mobile devices 202 may include one or more mobile devices,such as PDAs, cellular telephones, mobile computers, or any other mobilecomputing device. Cluster of mobile devices 202 may also include anyappliance (e.g., audio/video systems, microwaves, refrigerators, foodprocessors) containing a microprocessor (e.g., with spare processingtime), storage, or both. Application server 106 may instruct deviceswithin cluster of mobile devices 202 to perform computation, storage, orboth in a similar fashion as would have been distributed to multiplefixed cores by parallel computational framework 114 and the map/reducecomputational paradigm. Each device in cluster of mobile devices 202 mayperform a discrete computational job, storage job, or both. Applicationserver 106 may combine the results of each distributed job and return afinal result of the computation.

FIG. 3A shows illustrative data tables 300 used to support theconnectivity determinations of the present invention. One or more oftables 300 may be stored in, for example, a relational database in datastore 110 (FIG. 1). Table 302 may store an identification of all thenodes registered in the network community. A unique identifier may beassigned to each node and stored in table 302. In addition, a stringname may be associated with each node and stored in table 302.

As described above, in some embodiments, nodes may represent individualsor entities, in which case the string name may include the individual orperson's first and/or last name, nickname, handle, or entity name.

Table 304 may store user connectivity values. In some embodiments, userconnectivity values may be assigned automatically by the system (e.g.,by application server 106 (FIG. 1)). For example, application server 106(FIG. 1) may monitor all electronic interaction (e.g., electroniccommunication, electronic transactions, or both) between members of anetwork community. In some embodiments, a default user connectivityvalue (e.g., the link value 1) may be assigned initially to all links inthe network community. After electronic interaction is identifiedbetween two or more nodes in the network community, user connectivityvalues may be adjusted upwards or downwards depending on the type ofinteraction between the nodes and the result of the interaction. Forexample, each simple email exchange between two nodes may automaticallyincrease or decrease the user connectivity values connecting those twonodes by a fixed amount. More complicated interactions (e.g., product orservice sales or inquires) between two nodes may increase or decreasethe user connectivity values connecting those two nodes by some largerfixed amount. In some embodiments, user connectivity values between twonodes may always be increased unless a user or node indicates that theinteraction was unfavorable, not successfully completed, or otherwiseadverse. For example, a transaction may not have been timely executed oran email exchange may have been particularly displeasing. Adverseinteractions may automatically decrease user connectivity values whileall other interactions may increase user connectivity values (or have noeffect). In addition, user connectivity values may be automaticallyharvested using outside sources. For example, third-party data sources(such as ratings agencies and credit bureaus) may be automaticallyqueried for connectivity information. This connectivity information mayinclude completely objective information, completely subjectiveinformation, composite information that is partially objective andpartially subjective, any other suitable connectivity information, orany combination of the foregoing.

In some embodiments, user connectivity values may be manually assignedby members of the network community. These values may represent, forexample, the degree or level of trust between two users or nodes or onenode's assessment of another node's competence in some endeavor. Asdescribed above, user connectivity values may include a subjectivecomponent and an objective component in some embodiments. The subjectivecomponent may include a trustworthiness “score” indicative of howtrustworthy a first user or node finds a second user, node, community,or subcommunity. This score or value may be entirely subjective andbased on interactions between the two users, nodes, or communities. Acomposite user connectivity value including subjective and objectivecomponents may also be used. For example, third-party information may beconsulted to form an objective component based on, for example, thenumber of consumer complaints, credit score, socio-economic factors(e.g., age, income, political or religions affiliations, and criminalhistory), or number of citations/hits in the media or in search enginesearches. Third-party information may be accessed using communicationsnetwork 104 (FIG. 1). For example, a third-party credit bureau'sdatabase may be polled or a personal biography and backgroundinformation, including criminal history information, may be accessedfrom a third-party database or data source (e.g., as part of datasources 108 (FIG. 1) or a separate data source) or input directly by anode, user, or system administrator.

Table 304 may store an identification of a link head, link tail, anduser connectivity value for the link. Links may or may not bebidirectional. For example, a user connectivity value from node n₁ tonode n₂ may be different (and completely separate) than a link from noden₂ to node n₁. Especially in the trust context described above, eachuser can assign his or her own user connectivity value to a link (i.e.,two users need not trust each other an equal amount in someembodiments).

Table 306 may store an audit log of table 304. Table 306 may be analyzedto determine which nodes or links have changed in the network community.In some embodiments, a database trigger is used to automatically insertan audit record into table 306 whenever a change of the data in table304 is detected. For example, a new link may be created, a link may beremoved, or a user connectivity value may be changed. This audit log mayallow for decisions related to connectivity values to be madeprospectively (i.e., before an anticipated event). Such decisions may bemade at the request of a user, or as part of an automated process, suchas the processes described below with respect to FIG. 5. Thisprospective analysis may allow for the initiation of a transaction (ortaking of some particular action) in a fluid and/or dynamic manner.After such a change is detected, the trigger may automatically create anew row in table 306. Table 306 may store an identification of thechanged node, and identification of the changed link head, changed linktail, and the user connectivity value to be assigned to the changedlink. Table 306 may also store a timestamp indicative of the time of thechange and an operation code. In some embodiments, operation codes mayinclude “insert,” “update,” or “delete” operations, corresponding towhether a link was inserted, a user connectivity value was changed, or alink was deleted, respectively. Other operation codes may be used inother embodiments.

FIG. 3B shows illustrative data structure 310 used to support theconnectivity determinations of the present invention. In someembodiments, data structure 310 may be stored using key-value store 112(FIG. 1), while tables 300 are stored in data store 110 (FIG. 1). Asdescribed above, key-value store 112 (FIG. 1) may implement an HBasestorage system and include BigTable support. Like a traditionalrelational database management system, the data shown in FIG. 3B may bestored in tables. However, the BigTable support may allow for anarbitrary number of columns in each table, whereas traditionalrelational database management systems may require a fixed number ofcolumns.

Data structure 310 may include node table 312. In the example shown inFIG. 3B, node table 312 includes several columns. Node table 312 mayinclude row identifier column 314, which may store 64-bit, 128-bit,256-bit, 512-bit, or 1024-bit integers and may be used to uniquelyidentify each row (e.g., each node) in node table 312. Column 316 mayinclude a list of all the incoming links for the current node. Column318 may include a list of all the outgoing links for the current node.Column 320 may include a list of node identifiers to which the currentnode is connected. A first node may be connected to a second node ifoutgoing links may be followed to reach the second node. For example,for A->B, A is connected to B, but B may not be connected to A. Asdescribed in more detail below, column 320 may be used during theportion of process 400 (FIG. 4A) shown in FIG. 4B. Node table 312 mayalso include one or more “bucket” columns 322. These columns may store alist of paths that connect the current node to a target node. Asdescribed above, grouping paths by the last node in the path (e.g., thetarget node) may facilitate connectivity computations. As shown in FIG.3B, in some embodiments, to facilitate scanning, bucket column names mayinclude the target node identifier appended to the end of the “bucket:”column

FIGS. 4A-4D show illustrative processes for determining the connectivityof nodes within a network community. FIG. 4A shows process 400 forupdating a connectivity graph (or any other suitable data structure)associated with a network community. As described above, in someembodiments, each network community is associated with its ownconnectivity graph, digraph, tree, or other suitable data structure. Inother embodiments, a plurality of network communities may share one ormore connectivity graphs (or other data structure).

In some embodiments, the processes described with respect to FIG. 4A-4Dmay be executed to make decisions prospectively (i.e., before ananticipated event). Such decisions may be made at the request of a user,or as part of an automated process, such as the processes describedbelow with respect to FIG. 5. This prospective analysis may allow forthe initiation of a transaction (or taking of some particular action) ina fluid and/or dynamic manner.

At step 402, a determination is made whether at least one node haschanged in the network community. As described above, an audit recordmay be inserted into table 306 (FIG. 3) after a node has changed. Byanalyzing table 306 (FIG. 3), a determination may be made (e.g., byapplication server 106 of FIG. 1) that a new link has been added, anexisting link has been removed, or a user connectivity value haschanged. If, at step 404, it is determined that a node has changed, thenprocess 400 continues to step 410 (shown in FIG. 4B) to prepare thechanged nodes, step 412 (shown in FIG. 4C) to calculate pathsoriginating from the changed nodes, step 414 (shown in FIG. 4D) toremove paths that go through a changed node, and step 416 (shown in FIG.4E) to calculate paths that go through a changed node. It should benoted that more than one step or task shown in FIGS. 4B, 4C, 4D, and 4Emay be performed in parallel using, for example, a cluster of cores. Forexample, multiple steps or tasks shown in FIG. 4B may be executed inparallel or in a distributed fashion, then multiple steps or tasks shownin FIG. 4C may be executed in parallel or in a distributed fashion, thenmultiple steps or tasks shown in FIG. 4D may be executed in parallel orin a distributed fashion, and then multiple steps or tasks shown in FIG.4E may be executed in parallel or in a distributed fashion. In this way,overall latency associated with process 400 may be reduced.

If a node change is not detected at step 404, then process 400 enters asleep mode at step 406. For example, in some embodiments, an applicationthread or process may continuously check to determine if at least onenode or link has changed in the network community. In other embodiments,the application thread or process may periodically check for changedlinks and nodes every n seconds, where n is any positive number. Afterthe paths are calculated that go through a changed node at step 416 orafter a period of sleep at step 406, process 400 may determine whetheror not to loop at step 408. For example, if all changed nodes have beenupdated, then process 400 may stop at step 418. If, however, there aremore changed nodes or links to process, then process 400 may loop atstep 408 and return to step 404.

In practice, one or more steps shown in process 400 may be combined withother steps, performed in any suitable order, performed in parallel(e.g., simultaneously or substantially simultaneously), or removed.

FIGS. 4B-4E each include processes with a “map” phase and “reduce”phase. As described above, these phases may form part of a map/reducecomputational paradigm carried out by parallel computational framework114 (FIG. 1), key-value store 112 (FIG. 1), or both. As shown in FIG.4B, in order to prepare any changed nodes, map phase 420 may includedetermining if there are any more link changes at step 422, retrievingthe next link change at step 440, mapping the tail to out-link change atstep 442, and mapping the head to in-link change at step 444.

If there are no more link changes at step 422, then, in reduce phase424, a determination may be made at step 426 that there are more nodesand link changes to process. If so, then the next node and its linkchanges may be retrieved at step 428. The most recent link changes maybe preserved at step 430 while any intermediate link changes arereplaced by more recent changes. For example, the timestamp stored intable 306 (FIG. 3) may be used to determine the time of every link ornode change. At step 432, the average out-link user connectivity valuemay be calculated. For example, if node n₁ has eight out-links withassigned user connectivity values, these eight user connectivity valuesmay be averaged at step 432. At step 434, each out-links weight may becalculated in accordance with equation (1) above. All the out-linkweights may then be summed and used to normalize each out-link weight atstep 436. For example, each out-link weight may be divided by the sum ofall out-link weights. This may yield a weight between 0 and 1 for eachout-link. At step 438, the existing buckets for the changed node,in-links, and out-links may be saved. For example, the buckets may besaved in key-value store 112 (FIG. 1) or data store 110 (FIG. 1). Ifthere are no more nodes and link changes to process at step 426, theprocess may stop at step 446.

As shown in FIG. 4C, in order to calculate paths originating fromchanged nodes, map phase 448 may include determining if there are anymore changed nodes at step 450, retrieving the next changed node at step466, marking existing buckets for deletion by mapping changed nodes tothe NULL path at step 468, recursively generating paths by followingout-links at step 470, and if the path is a qualified path, mapping thetail to the path. Qualified paths may include paths that satisfy one ormore predefined threshold functions. For example, a threshold functionmay specify a minimum path weight. Paths with path weights greater thanthe minimum path weight may be designated as qualified paths.

If there are no more changed nodes at step 450, then, in reduce phase452, a determination may be made at step 454 that there are more nodesand paths to process. If so, then the next node and its paths may beretrieved at step 456. At step 458, buckets may be created by groupingpaths by their head. If a bucket contains only the NULL path at step460, then the corresponding cell in the node table may be deleted atstep 462. If the bucket contains more than the NULL path, then at step464 the bucket is saved to the corresponding cell in the node table. Ifthere are no more nodes and paths to process at step 456, the processmay stop at step 474.

As shown in FIG. 4D, in order to remove paths that go through a changednode, map phase 476 may include determining if there are any morechanged nodes at step 478 and retrieving the next changed node at step488. At step 490, the “bucket:” column in the node table (e.g., column322 of node table 312 (both of FIG. 3B)) corresponding to the changednode may be scanned. For example, as described above, the target nodeidentifier may be appended to the end of the “bucket:” column name. Eachbucket may include a list of paths that connect the current node to thetarget node (e.g., the changed node). At step 492, for each matchingnode found by the scan and the changed node's old buckets, the matchingnode may be matched to a (changed node, old bucket) deletion pair.

If there are no more changed nodes at step 478, then, in reduce phase480, a determination may be made at step 484 that there are more nodeand deletion pairs to process. If so, then the next node and itsdeletion pairs may be retrieved at step 484. At step 486, for eachdeletion pair, any paths that go through the changed node in the oldbucket may be deleted. If there are no more nodes and deletion pairs toprocess at step 482, the process may stop at step 494.

As shown in FIG. 4E, in order to calculate paths that go through achanged node, map phase 496 may include determining if there are anymore changed nodes at step 498 and retrieving the next changed node atstep 508. At step 510, the “bucket:” column in the node table (e.g.,column 322 of node table 312 (both of FIG. 3B)) corresponding to thechanged node may be scanned. At step 512, for each matching node foundin the scan and the changed node's paths, all paths in the scannedbucket may be joined with all paths of the changed bucket. At step 514,each matching node may be mapped to each qualified joined

If there are no more changed nodes at step 498, then, in reduce phase500, a determination may be made at step 502 that there are more nodeand paths to process. If so, then the next node and its paths may beretrieved at step 504. Each path may then be added to the appropriatenode bucket at step 506. If there are no more nodes and paths to processat step 502, the process may stop at step 516.

FIG. 5 shows illustrative process 520 for supporting a user query forall paths from a first node to a target node. For example, a first node(representing, for example, a first individual or entity) may wish toknow how connected the first node is to some second node (representing,for example, a second individual or entity) in the network community. Inthe context of trust described above (and where the user connectivityvalues represent, for example, at least partially subjective user trustvalues), this query may return an indication of how much the first nodemay trust the second node. In general, the more paths connecting the twonodes may yield a greater (or lesser if, for example, adverse ratingsare used) network connectivity value (or network trust amount).

At step 522, the node table cell where the row identifier equals thefirst node identifier and the column equals the target node identifierappended to the “bucket:” column name prefix is accessed. All paths maybe read from this cell at step 524. The path weights assigned to thepaths read at step 524 may then be summed at step 526. At step 528, thepath weights may be normalized by dividing each path weight by thecomputed sum of the path weights. A network connectivity value may thenbe computed at step 530. For example, each path's user connectivityvalue may be multiplied by its normalized path weight. The networkconnectivity value may then be computed in some embodiments inaccordance with:

t _(network) =Σt _(path) ×w _(path)  (4)

where t_(path) is the user connectivity value for a path (given inaccordance with equation (3)) and w_(path) is the normalized weight forthat path. The network connectivity value may then be held or outputted(e.g., displayed on a display device, output by processing circuitry ofapplication server 106, and/or stored on data store 110 (FIG. 1)). Inaddition, a decision-making algorithm may access the networkconnectivity value in order to make automatic decisions (e.g., automaticnetwork-based decisions, such as authentication or identity requests) onbehalf of the user. Network connectivity values may additionally oralternatively be outputted to external systems and processes located atthird-parties. The external systems and processes may be configured toautomatically initiate a transaction (or take some particular course ofaction) based, at least in part, on the received network connectivityvalues. Process 520 may stop at step 532.

In practice, one or more steps shown in process 520 may be combined withother steps, performed in any suitable order, performed in parallel(e.g., simultaneously or substantially simultaneously), or removed. Inaddition, as described above, various threshold functions may be used inorder to reduce computational complexity. For example, a thresholdfunction defining the maximum number of links to traverse may bedefined. Paths containing more than the threshold specified by thethreshold function may not be considered in the network connectivitydetermination. In addition, various threshold functions relating to linkand path weights may be defined. Links or paths below the thresholdweight specified by the threshold function may not be considered in thenetwork connectivity determination.

Although process 520 describes a single user query for all paths from afirst node to a target node, in actual implementations groups of nodesmay initiate a single query for all the paths from each node in thegroup to a particular target node. For example, multiple members of anetwork community may all initiate a group query to a target node.Process 520 may return an individual network connectivity value for eachquerying node in the group or a single composite network connectivityvalue taking into account all the nodes in the querying group. Forexample, the individual network connectivity values may be averaged toform a composite value or some weighted average may be used. The weightsassigned to each individual network connectivity value may be based on,for example, seniority in the community (e.g., how long each node hasbeen a member in the community), rank, or social stature. In addition,in some embodiments, a user may initiate a request for networkconnectivity values for multiple target nodes in a single query. Forexample, node n₁ may wish to determine network connectivity valuesbetween it and multiple other nodes. For example, the multiple othernodes may represent several candidates for initiating a particulartransaction with node n₁. By querying for all the network connectivityvalues in a single query, the computations may be distributed in aparallel fashion to multiple cores so that some or all of the resultsare computed substantially simultaneously.

In addition, queries may be initiated in a number of ways. For example,a user (represented by a source node) may identify another user(represented by a target node) in order to automatically initiateprocess 520. A user may identify the target node in any suitable way,for example, by selecting the target node from a visual display, graph,or tree, by inputting or selecting a username, handle, network address,email address, telephone number, geographic coordinates, or uniqueidentifier associated with the target node, or by speaking apredetermined command (e.g., “query node 1” or “query node group 1, 5,9” where 1, 5, and 9 represent unique node identifiers). After anidentification of the target node or nodes is received, process 520 maybe automatically executed. The results of the process (e.g., theindividual or composite network connectivity values) may then beautomatically sent to one or more third-party services or processes asdescribed above.

In an embodiment, a user may utilize access application 102 to generatea user query that is sent to access application server 106 overcommunications network 104 (see also, FIG. 1) and automatically initiateprocess 520. For example, a user may access an Apple iOS, Android, orWebOS application or any suitable application for use in accessingapplication 106 over communications network 104. The application maydisplay a searchable list of relationship data related to that user(e.g., “friend” or “follower” data) from one or more of Facebook,MySpace, openSocial, Friendster, Bebo, hi5, Orkut, PerfSpot, Yahoo! 360,LinkedIn, Twitter, Google Buzz, Really Simple Syndication readers or anyother social networking website or information service. In someembodiments, a user may search for relationship data that is not readilylisted—i.e., search Facebook, Twitter, or any suitable database ofinformation for target nodes that are not displayed in the searchablelist of relationship data. A user may select a target node as describedabove (e.g., select an item from a list of usernames representing a“friend” or “follower”) to request a measure of how connected the useris to the target node. Using the processes described with respect toFIGS. 3 and 4A-D, this query may return an indication of how much theuser may trust the target node. The returned indication may be displayedto the user using any suitable indicator. In some embodiments, indicatormay be a percentage that indicates how trustworthy the target node is tothe user.

In some embodiments, a user may utilize access application 102 toprovide manual assignments of at least partially subjective indicationsof how trustworthy the target node is. For example, the user may specifythat he or she trusts a selected target node (e.g., a selected “friend”or “follower”) to a particular degree. The particular degree may be inthe form of a percentage that represents the user's perception of howtrustworthy the target node is. The user may provide this indicationbefore, after, or during process 520 described above. The indicationprovided by the user (e.g., the at least partially subjectiveindications of trustworthiness) may then be automatically sent to one ormore third-party services or processes as described above. In someembodiments, the indications provided by the user may cause a nodeand/or link to change in a network community. This change may cause adetermination to be made that at least one node and/or link has changedin the network community, which in turn triggers various processes asdescribed with respect to FIGS. 3 and 4A-4D.

In some embodiments, a path counting approach may be used in addition toor in place of the weighted link approach described above. Processingcircuitry (e.g., of application server 106) may be configured to countthe number of paths between a first node n₁ and a second node n₂ withina network community. A connectivity rating R_(n1n2) may then be assignedto the nodes. The assigned connectivity rating may be proportional tothe number of paths, or relationships, connecting the two nodes. A pathwith one or more intermediate nodes between the first node n₁ and thesecond node n₂ may be scaled by an appropriate number (e.g., the numberof intermediate nodes) and this scaled number may be used to calculatethe connectivity rating.

Each equation presented above should be construed as a class ofequations of a similar kind, with the actual equation presented beingone representative example of the class. For example, the equationspresented above include all mathematically equivalent versions of thoseequations, reductions, simplifications, normalizations, and otherequations of the same degree.

The above described embodiments of the invention are presented forpurposes of illustration and not of limitation. The following numberedparagraphs give additional embodiments of the present invention.

What is claimed is:
 1. A method for determining the network connectivitybetween a first node and a second node connected to the first node by atleast one path, the method comprising: identifying paths to the secondnode from the first node within a network community; using processingcircuitry to: determine a normalized path weight for each identifiedpath; determine a user connectivity value for each identified path; foreach identified path, sum the product of the user connectivity value andthe normalized path weight to produce a network connectivity indication;and output the network connectivity indication.